Protected Geographical Indication Discrimination of Zhejiang and Non-Zhejiang Ophiopogonis japonicus by Near-Infrared (NIR) Spectroscopy Combined with Chemometrics: The Influence of Different Stoichiometric and Spectrogram Pretreatment Methods

Abstract

This paper presents a method for the protected geographical indication discrimination of Ophiopogon japonicus from Zhejiang and elsewhere using near-infrared (NIR) spectroscopy combined with chemometrics. A total of 3657 Ophiopogon japonicus samples from five major production areas in China were analyzed by NIR spectroscopy, and divided into 2127 from Zhejiang and 1530 from other areas (‘non-Zhejiang’). Principal component analysis (PCA) was selected to screen outliers and eliminate them. Monte Carlo cross validation (MCCV) was introduced to divide the training set and test set according to a ratio of 3:7. The raw spectra were preprocessed by nine single and partial combination methods such as the standard normal variable (SNV) and derivative, and then modeled by partial least squares regression (PLSR), a support vector machine (SVM), and soft independent modeling of class analogies (SIMCA). The effects of different pretreatment and chemometrics methods on the model are discussed. The results showed that the three pattern recognition methods were effective in geographical origin tracing, and selecting the appropriate preprocessing method could improve the traceability accuracy. The accuracy of PLSR after the standard normal variable was better, with R2 reaching 0.9979, while that of the second derivative was the lowest with an R2 of 0.9656. After the SNV pretreatment, the accuracy of the training set and test set of SVM reached the highest values, which were 99.73% and 98.40%, respectively. The accuracy of SIMCA pretreated with SNV and MSC was the highest for the origin traceability of Ophiopogon japonicus, which could reach 100%. The distance between the two classification models of SIMCA-SNV and SIMCA-MSC is greater than 3, indicating that the SIMCA model has good performance.

Simultaneous Quantification and Visualization of Photosynthetic Pigments in Lycopersicon esculentum Mill. under Different Levels of Nitrogen Application with Visible-Near Infrared Hyperspectral Imaging Technology

“…The Savitzky–Golay smoothing filter (S–G) is capable of mitigating the impact of interfering factors, such as the signal-to-noise ratio and dark current, thereby enhancing the accuracy and reliability of the data analysis [ 44 , 45 ]. The standard normal variable (SNV) is primarily employed to eliminate the effects of particle size, surface scattering and light path variation on diffuse reflection spectra [ 18 , 46 ]. Therefore, the effective spectrum was preprocessed using the S–G, SNV and S–G + SNV approaches, which reduced interference before modeling and effectively improved the prediction accuracy of the model, as shown in Figure 6 .…”

Rapid Measurement of Total Saponins, Mannitol, and Naringenin in Dendrobium officinale by Near-Infrared Spectroscopy and Chemometrics

“…However, the NIR spectrum is characterized by complex and overlapping peaks and useless information from background and noise, resulting in difficulty in clearly distinguishing the specific spectral range corresponding to the biochemical substance. To enhance model accuracy, researchers commonly employ spectral pretreatment methods such as standard normal variable (SNV) transformation, Savitzky-Golay (SG) smoothing, first derivative (1D), second derivative (2D), and multivariate scattering correction (MSC) to remove noise interference, linearity correction, and spectral fitting [16]. Wavelength selection methods are crucial for feature extraction.…”